# 1.Download the Employee Attrition Dataset
https://www.kaggle.com/datasets/patelprashant/employee-attrition
2.Perfrom Data Preprocessing
3.Model Building using Logistic Regression, Decision Tree and random forest
4.Calculate Performance metrics

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df=pd.read_csv("WA_Fn-UseC_-HR-Employee-Attrition.csv")

df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

df.shape

(1470, 35)

df.Attrition.value_counts()

No     1233
Yes     237
Name: Attrition, dtype: int64

df.describe()       #descriptive statistics

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

8 rows × 26 columns

df.info()  

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB

handle null values

df.isnull().any()

Age                         False
Attrition                   False
BusinessTravel              False
DailyRate                   False
Department                  False
DistanceFromHome            False
Education                   False
EducationField              False
EmployeeCount               False
EmployeeNumber              False
EnvironmentSatisfaction     False
Gender                      False
HourlyRate                  False
JobInvolvement              False
JobLevel                    False
JobRole                     False
JobSatisfaction             False
MaritalStatus               False
MonthlyIncome               False
MonthlyRate                 False
NumCompaniesWorked          False
Over18                      False
OverTime                    False
PercentSalaryHike           False
PerformanceRating           False
RelationshipSatisfaction    False
StandardHours               False
StockOptionLevel            False
TotalWorkingYears           False
TrainingTimesLastYear       False
WorkLifeBalance             False
YearsAtCompany              False
YearsInCurrentRole          False
YearsSinceLastPromotion     False
YearsWithCurrManager        False
dtype: bool

df.isnull().sum()

Age                         0
Attrition                   0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64

Data Visualisation

sns.distplot(df["TotalWorkingYears"])

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='TotalWorkingYears', ylabel='Density'>

sns.histplot(data=df, x='Age', hue='Attrition', kde=True)

<AxesSubplot:xlabel='Age', ylabel='Count'>

sns.countplot(x='JobSatisfaction', hue='Attrition', data=df)

<AxesSubplot:xlabel='JobSatisfaction', ylabel='count'>

sns.histplot(data=df, x='MonthlyIncome', hue='Attrition', kde=True)

<AxesSubplot:xlabel='MonthlyIncome', ylabel='Count'>

sns.distplot(df["TotalWorkingYears"])

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='TotalWorkingYears', ylabel='Density'>

plt.scatter(df["TotalWorkingYears"],df["YearsInCurrentRole"])

<matplotlib.collections.PathCollection at 0x1ebf8dff370>

plt.figure(figsize=(20, 15))
sns.heatmap(df.corr(),annot=True)

<AxesSubplot:>

sns.pairplot(df)    

<seaborn.axisgrid.PairGrid at 0x1ebf9972280>

Outlier Detection

sns.boxplot(df.YearsSinceLastPromotion) 

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='YearsSinceLastPromotion'>

sns.boxplot(df.DailyRate) 

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='DailyRate'>

sns.boxplot(df['Age'])    # NO OUTLIERS

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Age'>

sns.boxplot(df.YearsSinceLastPromotion) 

C:\Users\ACER\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='YearsSinceLastPromotion'>

df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

Splitting Dependent and Independent variables

x=df.drop(columns="Attrition",axis=1)
x.head()

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	...	1	80	0	8	0	1	6	4	0	5
1	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	...	4	80	1	10	3	3	10	7	1	7
2	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	...	2	80	0	7	3	3	0	0	0	0
3	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	...	3	80	0	8	3	3	8	7	3	0
4	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	...	4	80	1	6	3	3	2	2	2	2

5 rows × 34 columns

#dependent var
y=df.Attrition
y.head()

0    Yes
1     No
2    Yes
3     No
4     No
Name: Attrition, dtype: object

x.shape

(1470, 34)

y.shape

(1470,)

Encoding

from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()

df.info()  

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB

columns=["BusinessTravel","Department","EducationField","Gender","JobRole","MaritalStatus","Over18","OverTime"]
df[columns]=df[columns].apply(le.fit_transform)

x=df.drop(columns="Attrition",axis=1)
x.head()

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	2	1102	2	1	2	1	1	1	2	...	1	80	0	8	0	1	6	4	0	5
1	49	1	279	1	8	1	1	1	2	3	...	4	80	1	10	3	3	10	7	1	7
2	37	2	1373	1	2	2	4	1	4	4	...	2	80	0	7	3	3	0	0	0	0
3	33	1	1392	1	3	4	1	1	5	4	...	3	80	0	8	3	3	8	7	3	0
4	27	2	591	1	2	1	3	1	7	1	...	4	80	1	6	3	3	2	2	2	2

5 rows × 34 columns

#dependent var
y=df.Attrition
y.head()

0    Yes
1     No
2    Yes
3     No
4     No
Name: Attrition, dtype: object

x.shape

(1470, 34)

y.shape

(1470,)

Feature scaling

#feature scaling
from sklearn.preprocessing import MinMaxScaler
ms=MinMaxScaler()
x_scaled=pd.DataFrame(ms.fit_transform(x),columns=x.columns)
x_scaled.head()

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0.547619	1.0	0.715820	1.0	0.000000	0.25	0.2	0.0	0.000000	0.333333	...	0.000000	0.0	0.000000	0.200	0.0	0.000000	0.15	0.222222	0.000000	0.294118
1	0.738095	0.5	0.126700	0.5	0.250000	0.00	0.2	0.0	0.000484	0.666667	...	1.000000	0.0	0.333333	0.250	0.5	0.666667	0.25	0.388889	0.066667	0.411765
2	0.452381	1.0	0.909807	0.5	0.035714	0.25	0.8	0.0	0.001451	1.000000	...	0.333333	0.0	0.000000	0.175	0.5	0.666667	0.00	0.000000	0.000000	0.000000
3	0.357143	0.5	0.923407	0.5	0.071429	0.75	0.2	0.0	0.001935	1.000000	...	0.666667	0.0	0.000000	0.200	0.5	0.666667	0.20	0.388889	0.200000	0.000000
4	0.214286	1.0	0.350036	0.5	0.035714	0.00	0.6	0.0	0.002903	0.000000	...	1.000000	0.0	0.333333	0.150	0.5	0.666667	0.05	0.111111	0.133333	0.117647

5 rows × 34 columns

Splitting Data into Train and Test.

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x_scaled,y,test_size=0.2,random_state=0)

x_train.shape,x_test.shape,y_train.shape,y_test.shape

((1176, 34), (294, 34), (1176,), (294,))

x_train.head()

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1374	0.952381	1.0	0.360057	1.0	0.714286	0.50	0.2	0.0	0.937107	1.000000	...	0.666667	0.0	0.333333	0.725	0.333333	0.333333	0.025	0.000000	0.000000	0.000000
1092	0.642857	1.0	0.607015	0.5	0.964286	0.50	1.0	0.0	0.747460	1.000000	...	1.000000	0.0	0.333333	0.200	0.500000	0.666667	0.125	0.222222	0.000000	0.176471
768	0.523810	1.0	0.141732	1.0	0.892857	0.50	0.4	0.0	0.515239	0.666667	...	0.333333	0.0	0.333333	0.200	0.500000	0.333333	0.175	0.388889	0.466667	0.294118
569	0.428571	0.0	0.953472	1.0	0.250000	0.75	0.2	0.0	0.381229	0.000000	...	0.333333	0.0	0.000000	0.250	0.166667	0.666667	0.250	0.388889	0.000000	0.529412
911	0.166667	0.5	0.355762	1.0	0.821429	0.00	0.2	0.0	0.615385	0.666667	...	1.000000	0.0	0.000000	0.025	0.666667	0.666667	0.025	0.000000	0.066667	0.000000

5 rows × 34 columns

• Model Building-logistic regression

o   Import the model building Libraries
o   Initializing the model
o   Training and testing the model
o   Evaluation of Model
o   Save the Model

from sklearn.linear_model import LogisticRegression
model=LogisticRegression()

model.fit(x_train,y_train)

LogisticRegression()

pred=model.predict(x_test)
pred

array(['No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No'],
      dtype=object)

y_test

442      No
1091     No
981     Yes
785      No
1332    Yes
       ... 
1439     No
481      No
124     Yes
198      No
1229     No
Name: Attrition, Length: 294, dtype: object

#evlauation metrics
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve

accuracy_score(y_test,pred)

0.8843537414965986

confusion_matrix(y_test,pred)

array([[242,   3],
       [ 31,  18]], dtype=int64)

pd.crosstab(y_test,pred)

col_0	No	Yes
Attrition
No	242	3
Yes	31	18

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.89      0.99      0.93       245
         Yes       0.86      0.37      0.51        49

    accuracy                           0.88       294
   macro avg       0.87      0.68      0.72       294
weighted avg       0.88      0.88      0.86       294

probability=model.predict_proba(x_test)[:,1]
probability

array([0.16000127, 0.20600667, 0.31532384, 0.09242886, 0.63667551,
       0.06153061, 0.61819432, 0.0757087 , 0.00841372, 0.3912069 ,
       0.05398439, 0.33293123, 0.02020698, 0.67215483, 0.19786547,
       0.03454902, 0.11043981, 0.17101703, 0.04477777, 0.22783614,
       0.2335018 , 0.01553905, 0.06464492, 0.05029956, 0.58792413,
       0.44849464, 0.07412714, 0.04460935, 0.67666632, 0.0584383 ,
       0.01599026, 0.03521098, 0.06963085, 0.17397462, 0.07830857,
       0.04288032, 0.08150424, 0.07106342, 0.03622137, 0.05223965,
       0.04862098, 0.02091497, 0.01819361, 0.01362467, 0.02873997,
       0.50236969, 0.41553218, 0.00306874, 0.73976412, 0.51382382,
       0.09637213, 0.48845516, 0.08036228, 0.25757243, 0.66516772,
       0.26308027, 0.01964858, 0.30198497, 0.02919946, 0.16038964,
       0.02102747, 0.21670232, 0.13981568, 0.0358316 , 0.37208403,
       0.03002317, 0.29091186, 0.16041142, 0.10437497, 0.08695177,
       0.08217589, 0.30984518, 0.08531362, 0.07420689, 0.12268651,
       0.06192552, 0.04640904, 0.07624712, 0.19738483, 0.03236316,
       0.00884439, 0.0244108 , 0.13635803, 0.0260104 , 0.03341008,
       0.08186888, 0.00499397, 0.03474852, 0.03858027, 0.14602694,
       0.26167665, 0.16667357, 0.27400109, 0.24159565, 0.02160421,
       0.17748606, 0.34076078, 0.28022482, 0.06914126, 0.05003806,
       0.24437761, 0.74698271, 0.35438567, 0.01920627, 0.08778845,
       0.03255847, 0.05461351, 0.15123251, 0.06843702, 0.13752637,
       0.09584388, 0.04669882, 0.02493091, 0.15383171, 0.07081259,
       0.03089296, 0.0537667 , 0.11554316, 0.00881616, 0.01263271,
       0.17552253, 0.05045234, 0.08823238, 0.82995757, 0.03017756,
       0.0236819 , 0.0087012 , 0.1349589 , 0.16474801, 0.05202613,
       0.01524549, 0.29278083, 0.54767448, 0.34275448, 0.04629541,
       0.38966344, 0.61333366, 0.14552367, 0.07402366, 0.24143471,
       0.09418418, 0.0689069 , 0.10061956, 0.19346327, 0.20026293,
       0.03004939, 0.14900424, 0.00348846, 0.11225149, 0.15843155,
       0.06047573, 0.18601882, 0.06085869, 0.12221317, 0.03280184,
       0.02738799, 0.06356425, 0.08302382, 0.01541716, 0.014665  ,
       0.38517822, 0.01264231, 0.14961974, 0.80508787, 0.11598661,
       0.2842811 , 0.17020143, 0.1530583 , 0.02764153, 0.00613226,
       0.04191632, 0.09782393, 0.11551417, 0.10377982, 0.01779313,
       0.14371315, 0.10615435, 0.10298963, 0.05132621, 0.09061081,
       0.02897383, 0.09924087, 0.00512032, 0.75108423, 0.04296968,
       0.04062134, 0.37518972, 0.04563128, 0.7251816 , 0.10671665,
       0.36949086, 0.38146941, 0.32095493, 0.05266802, 0.08172004,
       0.13947833, 0.04334317, 0.01469593, 0.26413988, 0.06330966,
       0.1614747 , 0.15380517, 0.67152357, 0.05840793, 0.27891823,
       0.04512564, 0.46033865, 0.00348431, 0.14068967, 0.02747401,
       0.12714133, 0.17284246, 0.07341066, 0.10099827, 0.16870885,
       0.02560842, 0.01824031, 0.08670796, 0.02834237, 0.13710215,
       0.08778935, 0.2200061 , 0.73401148, 0.15938978, 0.4095449 ,
       0.01513845, 0.11306309, 0.21497506, 0.32337575, 0.03409266,
       0.04256318, 0.32157531, 0.05454465, 0.02348479, 0.16423352,
       0.32696147, 0.22892063, 0.00877159, 0.08198819, 0.01156361,
       0.1408691 , 0.29235147, 0.01270305, 0.17329916, 0.04081391,
       0.04094165, 0.42771425, 0.34958286, 0.03766772, 0.12025286,
       0.37698923, 0.3192629 , 0.79559338, 0.05385659, 0.21597037,
       0.06383728, 0.00570991, 0.66018187, 0.35855286, 0.37783606,
       0.36781398, 0.03554512, 0.21718203, 0.05943622, 0.06554485,
       0.10081475, 0.00818713, 0.26591316, 0.42809675, 0.06542835,
       0.09296803, 0.01259826, 0.14226651, 0.05072662, 0.02372258,
       0.02586923, 0.06760427, 0.24315648, 0.26961432, 0.19831733,
       0.2652296 , 0.0165923 , 0.15784236, 0.08398982, 0.02711775,
       0.18750547, 0.00783535, 0.2844239 , 0.00270742, 0.02484969,
       0.22585745, 0.72775605, 0.07691547, 0.26304359])

•Model Building-decision tree

o   Import the model building Libraries
o   Initializing the model
o   Training and testing the model
o   Evaluation of Model
o   Save the Model

from sklearn.tree import DecisionTreeClassifier
dtc=DecisionTreeClassifier()

dtc.fit(x_train,y_train)

DecisionTreeClassifier()

pred=dtc.predict(x_test)
pred

array(['No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'Yes', 'Yes',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes',
       'No', 'Yes', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'Yes',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'Yes',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'Yes', 'No', 'No',
       'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'Yes', 'No', 'No',
       'Yes', 'No', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No',
       'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No'], dtype=object)

y_test

442      No
1091     No
981     Yes
785      No
1332    Yes
       ... 
1439     No
481      No
124     Yes
198      No
1229     No
Name: Attrition, Length: 294, dtype: object

df

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	2	1102	2	1	2	1	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	1	279	1	8	1	1	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	2	1373	1	2	2	4	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	1	1392	1	3	4	1	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	2	591	1	2	1	3	1	7	...	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	No	1	884	1	23	2	3	1	2061	...	3	80	1	17	3	3	5	2	0	3
1466	39	No	2	613	1	6	1	3	1	2062	...	1	80	1	9	5	3	7	7	1	7
1467	27	No	2	155	1	4	3	1	1	2064	...	2	80	1	6	0	3	6	2	0	3
1468	49	No	1	1023	2	2	3	3	1	2065	...	4	80	0	17	3	2	9	6	0	8
1469	34	No	2	628	1	8	3	3	1	2068	...	1	80	0	6	3	4	4	3	1	2

1470 rows × 35 columns

from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve

accuracy_score(y_test,pred)

0.7448979591836735

confusion_matrix(y_test,pred)   

array([[202,  43],
       [ 32,  17]], dtype=int64)

pd.crosstab(y_test,pred)

col_0	No	Yes
Attrition
No	202	43
Yes	32	17

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.86      0.82      0.84       245
         Yes       0.28      0.35      0.31        49

    accuracy                           0.74       294
   macro avg       0.57      0.59      0.58       294
weighted avg       0.77      0.74      0.75       294

Probability=dtc.predict_proba(x_test)[:,1]
Probability

array([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
       0., 0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,
       0., 0., 0., 1., 0., 1., 0., 1., 1., 0., 0., 0., 1., 0., 0., 1., 0.,
       0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
       1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 0.,
       0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1.,
       0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,
       0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.,
       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
       0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 1., 0., 0., 0.,
       0., 0., 0., 0., 0.])

#roc curve
fpr,tpr,thresholds=roc_curve(y_test,Probability)

plt.plot(fpr,tpr)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC CURVE')
plt.show()

from sklearn import tree      #DECISION TREE
plt.figure(figsize=(25,15))
tree.plot_tree(dtc,filled=True)

[Text(457.3522928839733, 792.75, 'X[27] <= 0.038\ngini = 0.269\nsamples = 1176\nvalue = [988, 188]'),
 Text(111.78631051752922, 747.45, 'X[16] <= 0.75\ngini = 0.5\nsamples = 78\nvalue = [39, 39]'),
 Text(69.86644407345577, 702.15, 'X[4] <= 0.554\ngini = 0.426\nsamples = 39\nvalue = [27, 12]'),
 Text(46.57762938230384, 656.85, 'X[15] <= 0.167\ngini = 0.312\nsamples = 31\nvalue = [25, 6]'),
 Text(27.946577629382304, 611.55, 'X[21] <= 0.5\ngini = 0.49\nsamples = 7\nvalue = [3, 4]'),
 Text(18.631051752921536, 566.25, 'X[17] <= 0.056\ngini = 0.375\nsamples = 4\nvalue = [3, 1]'),
 Text(9.315525876460768, 520.95, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(27.946577629382304, 520.95, 'gini = 0.0\nsamples = 3\nvalue = [3, 0]'),
 Text(37.26210350584307, 566.25, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(65.20868113522538, 611.55, 'X[19] <= 0.056\ngini = 0.153\nsamples = 24\nvalue = [22, 2]'),
 Text(55.89315525876461, 566.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(74.52420701168614, 566.25, 'X[9] <= 0.167\ngini = 0.083\nsamples = 23\nvalue = [22, 1]'),
 Text(65.20868113522538, 520.95, 'X[28] <= 0.583\ngini = 0.5\nsamples = 2\nvalue = [1, 1]'),
 Text(55.89315525876461, 475.65, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(74.52420701168614, 475.65, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(83.83973288814691, 520.95, 'gini = 0.0\nsamples = 21\nvalue = [21, 0]'),
 Text(93.15525876460768, 656.85, 'X[22] <= 0.679\ngini = 0.375\nsamples = 8\nvalue = [2, 6]'),
 Text(83.83973288814691, 611.55, 'gini = 0.0\nsamples = 6\nvalue = [0, 6]'),
 Text(102.47078464106845, 611.55, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(153.70617696160267, 702.15, 'X[11] <= 0.364\ngini = 0.426\nsamples = 39\nvalue = [12, 27]'),
 Text(130.41736227045075, 656.85, 'X[17] <= 0.1\ngini = 0.133\nsamples = 14\nvalue = [1, 13]'),
 Text(121.10183639398998, 611.55, 'gini = 0.0\nsamples = 13\nvalue = [0, 13]'),
 Text(139.73288814691153, 611.55, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(176.9949916527546, 656.85, 'X[8] <= 0.105\ngini = 0.493\nsamples = 25\nvalue = [11, 14]'),
 Text(158.36393989983304, 611.55, 'X[22] <= 0.464\ngini = 0.278\nsamples = 6\nvalue = [5, 1]'),
 Text(149.0484140233723, 566.25, 'gini = 0.0\nsamples = 5\nvalue = [5, 0]'),
 Text(167.67946577629382, 566.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(195.6260434056761, 611.55, 'X[15] <= 0.5\ngini = 0.432\nsamples = 19\nvalue = [6, 13]'),
 Text(186.31051752921536, 566.25, 'gini = 0.0\nsamples = 7\nvalue = [0, 7]'),
 Text(204.9415692821369, 566.25, 'X[6] <= 0.4\ngini = 0.5\nsamples = 12\nvalue = [6, 6]'),
 Text(186.31051752921536, 520.95, 'X[3] <= 0.75\ngini = 0.278\nsamples = 6\nvalue = [5, 1]'),
 Text(176.9949916527546, 475.65, 'gini = 0.0\nsamples = 5\nvalue = [5, 0]'),
 Text(195.6260434056761, 475.65, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(223.57262103505843, 520.95, 'X[8] <= 0.249\ngini = 0.278\nsamples = 6\nvalue = [1, 5]'),
 Text(214.25709515859768, 475.65, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(232.8881469115192, 475.65, 'gini = 0.0\nsamples = 5\nvalue = [0, 5]'),
 Text(802.9182752504174, 747.45, 'X[21] <= 0.5\ngini = 0.235\nsamples = 1098\nvalue = [949, 149]'),
 Text(457.6979862270451, 702.15, 'X[29] <= 0.167\ngini = 0.162\nsamples = 798\nvalue = [727, 71]'),
 Text(251.51919866444075, 656.85, 'X[8] <= 0.445\ngini = 0.38\nsamples = 47\nvalue = [35, 12]'),
 Text(232.8881469115192, 611.55, 'X[16] <= 0.75\ngini = 0.1\nsamples = 19\nvalue = [18, 1]'),
 Text(223.57262103505843, 566.25, 'gini = 0.0\nsamples = 18\nvalue = [18, 0]'),
 Text(242.20367278797997, 566.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(270.15025041736226, 611.55, 'X[17] <= 0.094\ngini = 0.477\nsamples = 28\nvalue = [17, 11]'),
 Text(260.8347245409015, 566.25, 'gini = 0.0\nsamples = 4\nvalue = [0, 4]'),
 Text(279.46577629382307, 566.25, 'X[8] <= 0.524\ngini = 0.413\nsamples = 24\nvalue = [17, 7]'),
 Text(270.15025041736226, 520.95, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(288.7813021702838, 520.95, 'X[33] <= 0.324\ngini = 0.351\nsamples = 22\nvalue = [17, 5]'),
 Text(270.15025041736226, 475.65, 'X[2] <= 0.025\ngini = 0.133\nsamples = 14\nvalue = [13, 1]'),
 Text(260.8347245409015, 430.35, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(279.46577629382307, 430.35, 'gini = 0.0\nsamples = 13\nvalue = [13, 0]'),
 Text(307.41235392320533, 475.65, 'X[2] <= 0.329\ngini = 0.5\nsamples = 8\nvalue = [4, 4]'),
 Text(298.0968280467446, 430.35, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(316.7278797996661, 430.35, 'X[19] <= 0.444\ngini = 0.32\nsamples = 5\nvalue = [4, 1]'),
 Text(307.41235392320533, 385.05, 'gini = 0.0\nsamples = 4\nvalue = [4, 0]'),
 Text(326.0434056761269, 385.05, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(663.8767737896494, 656.85, 'X[30] <= 0.963\ngini = 0.145\nsamples = 751\nvalue = [692, 59]'),
 Text(654.5612479131887, 611.55, 'X[30] <= 0.113\ngini = 0.143\nsamples = 750\nvalue = [692, 58]'),
 Text(483.82512520868113, 566.25, 'X[9] <= 0.167\ngini = 0.218\nsamples = 257\nvalue = [225, 32]'),
 Text(426.1853088480801, 520.95, 'X[33] <= 0.147\ngini = 0.355\nsamples = 65\nvalue = [50, 15]'),
 Text(395.90984974958263, 475.65, 'X[33] <= 0.029\ngini = 0.303\nsamples = 59\nvalue = [48, 11]'),
 Text(363.30550918196997, 430.35, 'X[12] <= 0.5\ngini = 0.463\nsamples = 22\nvalue = [14, 8]'),
 Text(344.6744574290484, 385.05, 'X[11] <= 0.179\ngini = 0.198\nsamples = 9\nvalue = [8, 1]'),
 Text(335.35893155258765, 339.75, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(353.9899833055092, 339.75, 'gini = 0.0\nsamples = 8\nvalue = [8, 0]'),
 Text(381.9365609348915, 385.05, 'X[11] <= 0.4\ngini = 0.497\nsamples = 13\nvalue = [6, 7]'),
 Text(372.6210350584307, 339.75, 'gini = 0.0\nsamples = 4\nvalue = [4, 0]'),
 Text(391.2520868113522, 339.75, 'X[4] <= 0.286\ngini = 0.346\nsamples = 9\nvalue = [2, 7]'),
 Text(381.9365609348915, 294.45000000000005, 'X[19] <= 0.944\ngini = 0.444\nsamples = 3\nvalue = [2, 1]'),
 Text(372.6210350584307, 249.14999999999998, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(391.2520868113522, 249.14999999999998, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
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 Text(1187.7295492487478, 566.25, 'X[4] <= 0.482\ngini = 0.128\nsamples = 146\nvalue = [136, 10]'),
 Text(1162.1118530884808, 520.95, 'X[30] <= 0.063\ngini = 0.038\nsamples = 104\nvalue = [102, 2]'),
 Text(1152.79632721202, 475.65, 'X[11] <= 0.193\ngini = 0.32\nsamples = 10\nvalue = [8, 2]'),
 Text(1143.4808013355594, 430.35, 'X[6] <= 0.7\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
 Text(1134.1652754590984, 385.05, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(1152.79632721202, 385.05, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(1162.1118530884808, 430.35, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
 Text(1171.4273789649417, 475.65, 'gini = 0.0\nsamples = 94\nvalue = [94, 0]'),
 Text(1213.347245409015, 520.95, 'X[9] <= 0.167\ngini = 0.308\nsamples = 42\nvalue = [34, 8]'),
 Text(1190.058430717863, 475.65, 'X[29] <= 0.833\ngini = 0.375\nsamples = 4\nvalue = [1, 3]'),
 Text(1180.7429048414024, 430.35, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(1199.3739565943238, 430.35, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(1236.636060100167, 475.65, 'X[0] <= 0.393\ngini = 0.229\nsamples = 38\nvalue = [33, 5]'),
 Text(1218.0050083472454, 430.35, 'X[17] <= 0.35\ngini = 0.5\nsamples = 6\nvalue = [3, 3]'),
 Text(1208.6894824707847, 385.05, 'X[12] <= 0.333\ngini = 0.375\nsamples = 4\nvalue = [3, 1]'),
 Text(1199.3739565943238, 339.75, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(1218.0050083472454, 339.75, 'gini = 0.0\nsamples = 3\nvalue = [3, 0]'),
 Text(1227.320534223706, 385.05, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(1255.2671118530884, 430.35, 'X[8] <= 0.992\ngini = 0.117\nsamples = 32\nvalue = [30, 2]'),
 Text(1245.9515859766277, 385.05, 'X[28] <= 0.917\ngini = 0.062\nsamples = 31\nvalue = [30, 1]'),
 Text(1236.636060100167, 339.75, 'gini = 0.0\nsamples = 30\nvalue = [30, 0]'),
 Text(1255.2671118530884, 339.75, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(1264.5826377295493, 385.05, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(1206.3606010016695, 566.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(1343.7646076794658, 611.55, 'X[14] <= 0.812\ngini = 0.456\nsamples = 57\nvalue = [37, 20]'),
 Text(1311.160267111853, 566.25, 'X[32] <= 0.4\ngini = 0.238\nsamples = 29\nvalue = [25, 4]'),
 Text(1292.5292153589317, 520.95, 'X[8] <= 0.071\ngini = 0.142\nsamples = 26\nvalue = [24, 2]'),
 Text(1283.2136894824707, 475.65, 'X[8] <= 0.019\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
 Text(1273.89816360601, 430.35, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(1292.5292153589317, 430.35, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(1301.8447412353923, 475.65, 'gini = 0.0\nsamples = 23\nvalue = [23, 0]'),
 Text(1329.7913188647747, 520.95, 'X[2] <= 0.324\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
 Text(1320.475792988314, 475.65, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(1339.1068447412354, 475.65, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(1376.3689482470784, 566.25, 'X[32] <= 0.1\ngini = 0.49\nsamples = 28\nvalue = [12, 16]'),
 Text(1367.0534223706177, 520.95, 'X[12] <= 0.833\ngini = 0.48\nsamples = 20\nvalue = [12, 8]'),
 Text(1357.737896494157, 475.65, 'X[4] <= 0.018\ngini = 0.415\nsamples = 17\nvalue = [12, 5]'),
 Text(1348.422370617696, 430.35, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(1367.0534223706177, 430.35, 'X[17] <= 0.365\ngini = 0.32\nsamples = 15\nvalue = [12, 3]'),
 Text(1357.737896494157, 385.05, 'gini = 0.0\nsamples = 11\nvalue = [11, 0]'),
 Text(1376.3689482470784, 385.05, 'X[4] <= 0.179\ngini = 0.375\nsamples = 4\nvalue = [1, 3]'),
 Text(1367.0534223706177, 339.75, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(1385.6844741235393, 339.75, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(1376.3689482470784, 475.65, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(1385.6844741235393, 520.95, 'gini = 0.0\nsamples = 8\nvalue = [0, 8]')]

from sklearn.model_selection import GridSearchCV     #GridSearchCV for tuning parameters
parameter={
 'criterion':['gini','entropy'],
  'splitter':['best','random'],
  'max_depth':[1,2,3,4,5],
  'max_features':['auto', 'sqrt', 'log2']

}

grid_search=GridSearchCV(estimator=dtc,param_grid=parameter,cv=5,scoring="accuracy")

grid_search.fit(x_train,y_train)

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [1, 2, 3, 4, 5],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'splitter': ['best', 'random']},
             scoring='accuracy')

grid_search.best_params_

{'criterion': 'gini',
 'max_depth': 3,
 'max_features': 'sqrt',
 'splitter': 'best'}

dtc_cv=DecisionTreeClassifier(
criterion='gini',
 max_depth = 5,
 max_features= 'auto',
 splitter= 'best')
dtc_cv.fit(x_train,y_train)

DecisionTreeClassifier(max_depth=5, max_features='auto')

pred=dtc_cv.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.85      0.94      0.90       245
         Yes       0.39      0.18      0.25        49

    accuracy                           0.82       294
   macro avg       0.62      0.56      0.57       294
weighted avg       0.78      0.82      0.79       294

•Model Building-Random forest

o   Import the model building Libraries
o   Initializing the model
o   Training and testing the model
o   Evaluation of Model
o   Save the Model

# combination of all decision trees
from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier()

forest_params = [{'max_depth': list(range(10, 15)), 'max_features': list(range(0,14))}]

from sklearn.model_selection import GridSearchCV     #GridSearchCV for tuning parameters

rfc_cv=GridSearchCV(rfc,forest_params,cv=10,scoring="accuracy")

rfc_cv.fit(x_train,y_train)

C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 898, in fit
    super().fit(
  File "C:\Users\ACER\anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\Users\ACER\anaconda3\lib\site-packages\sklearn\model_selection\_search.py:918: UserWarning: One or more of the test scores are non-finite: [       nan 0.84778357 0.85119513 0.85885122 0.85882949 0.85966971
 0.85967695 0.85543966 0.85969144 0.86137911 0.86052441 0.85627988
 0.85543242 0.86137911        nan 0.84864552 0.85374475 0.85713458
 0.85545415 0.85543242 0.85967695 0.85543242 0.85883674 0.86221208
 0.85457048 0.85882949 0.85797479 0.85712009        nan 0.85714182
 0.85544691 0.86309576 0.85798928 0.86223381 0.85714182 0.85711285
 0.86393597 0.85798204 0.86220484 0.86053165 0.85796755 0.85882225
        nan 0.85119513 0.85629436 0.85969144 0.85798928 0.85457048
 0.86137911 0.8605389  0.85967695 0.86137187 0.85797479 0.86054614
 0.85713458 0.85882949        nan 0.84693611 0.85545415 0.85798204
 0.85799652 0.86394321 0.86138635 0.85798204 0.85796031 0.86306678
 0.86306678 0.85712734 0.8596842  0.85882225]
  warnings.warn(

GridSearchCV(cv=10, estimator=RandomForestClassifier(),
             param_grid=[{'max_depth': [10, 11, 12, 13, 14],
                          'max_features': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                           12, 13]}],
             scoring='accuracy')

pred=rfc_cv.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.86      0.99      0.92       245
         Yes       0.80      0.16      0.27        49

    accuracy                           0.85       294
   macro avg       0.83      0.58      0.59       294
weighted avg       0.85      0.85      0.81       294